code: def _model_predict_keras(input_data, input_shape):
      model = tf.keras.models.Sequential()
      model.add(tf.keras.layers.Dense(2, batch_input_shape=input_shape))
      model.add(tf.keras.layers.Dense(3))
  
      weights = model.get_weights()
      config = model.get_config()
      out = model(input_data)
  
      return out, weights, config
  
file_path: tf-encrypted/tf_encrypted/keras/models/sequential_test.py
imports: import unittest
  import numpy
  import tensorflow
  import tf_encrypted
  from tf_encrypted.keras import Sequential
  from tf_encrypted.keras.layers import Dense
label_desc: Use image node_id 1 for calling a global function with example usage: _model_predict_keras(input_data, input_shape) and returns: out,  weights,  config